Data Analyst – Underwriting Analytics

Jobtailor; Dublin; On-site; EUR 38,000 - 52,000; Full time

Job summary

Jobtailor in Ireland (Dublin area) seeks a junior analyst to join the Underwriting Analytics team. You will produce management information across underwriting KPIs, design dashboards, and build forecasting models to support planning and portfolio decisions.

You will work with data teams to improve data quality, automate recurring reports, and deliver actionable insights to optimize underwriting, pricing and profitability.

Qualifications

• Bachelor's degree in a quantitative field.
• N/A
• 0–3 years of data analysis, reporting or analytics experience; insurance experience is a plus.
• Strong numeracy and the ability to work with large datasets and complex calculations.
• Excel proficiency including formulas, pivot tables and basic data manipulation.
• Proficiency in PowerPoint and Word for reports and documentation.
• Exposure to Python and/or R.

Responsibilities

• Produce high-quality MI across underwriting KPIs (premium, loss ratio, retention, new business).
• Design, build and maintain reports and dashboards for underwriting and pricing teams.
• Build and maintain forecasting models for volume, premium and loss forecasts.
• Support underwriting analytics leadership with actionable insights.
• Analyze trends in claims, exposure, and pricing to identify opportunities and risks.
• Improve data quality through remediation and better data processes and controls.
• Automate recurring reports, data refreshes and quality checks.
• Document processes, assumptions and methods clearly and repeatably.
• Collaborate with Underwriting, Pricing, Finance and Data teams to deliver practical solutions.
• Contribute to ad-hoc analyses like profitability deep dives and pricing impact assessments.

Skills

Numeracy

Data literacy

Large datasets

Analytical thinking

Communication

Education

Bachelor's degree in quantitative field

Tools

Excel

PowerPoint

Word

Python

R

Power BI

Tableau

Databricks

EMBlem

Job description

Responsibilities

• Produce high-quality management information (MI) across a range of Underwriting and Performance KPIs (e.g. premium, loss ratio, retention, new business, rate changes).
• Design, build and maintain regular reports and dashboards for stakeholders across the Underwriting and Actuarial Pricing functions.
• Build and maintain financial and performance forecasting models (e.g. volume, premium and loss forecasts) to support planning, budgeting and portfolio steering.
• Support the Head of Underwriting Analytics in generating actionable insights and recommendations for underwriting, pricing and portfolio management.
• Analyse trends in claims, exposure and pricing to identify opportunities and risks.
• Help improve data quality by identifying issues, contributing to data remediation and defining better data processes and controls.
• Enhance efficiency through process automation (for example, automating recurring reports, data refreshes and quality checks).
• Document processes, assumptions and methods in a clear, concise and repeatable way.
• Collaborate closely with Underwriting, Pricing, Finance and Data teams to understand requirements and deliver practical, user-friendly solutions.
• Contribute to ad-hoc analysis and projects (e.g. profitability deep dives, product reviews, portfolio segmentation, pricing change impact assessments).

Requirements

• Bachelor’s degree (or equivalent) in Mathematics, Statistics, Actuarial Science, Data Science, Finance, Economics, Engineering or a related quantitative discipline.
• New graduate or approximately 0–3 years of relevant experience in data analysis, reporting, analytics, actuarial, or a related field (insurance experience is an advantage but not essential).
• Strong numeracy and data literacy, with the ability to work with large datasets and complex calculations.
• Proficiency in Microsoft Excel, including use of formulas, pivot tables and basic data manipulation.
• Proficiency in Microsoft PowerPoint and Word for preparing reports, presentations and documentation.
• Exposure to, or basic knowledge of, at least one programming language such as Python and/or R.
• Experience with data visualisation and reporting tools such as Power BI or Tableau is an advantage.
• Familiarity with working in cloud‑based data environments (e.g. Databricks or similar) is an advantage.
• Knowledge of WTW tools such as EMBlem and Radar is an advantage but not required – training can be provided.
• Experience with statistical or analytical techniques (e.g. regression, time series, segmentation) is beneficial.